{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "first_steps_with_tensor_flow.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "ajVM7rkoYXeL",
        "ci1ISxxrZ7v0",
        "copyright-notice"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "copyright-notice",
        "colab_type": "text"
      },
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "id": "copyright-notice2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f3CKqFUqL2-",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " # 使用 TensorFlow 的基本步骤"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bd2Zkk1LE2Zr",
        "colab_type": "text"
      },
      "source": [
        " **学习目标：**\n",
        "  * 学习基本的 TensorFlow 概念\n",
        "  * 在 TensorFlow 中使用 `LinearRegressor` 类并基于单个输入特征预测各城市街区的房屋价值中位数\n",
        "  * 使用均方根误差 (RMSE) 评估模型预测的准确率\n",
        "  * 通过调整模型的超参数提高模型准确率"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxiIKhP4E2Zr",
        "colab_type": "text"
      },
      "source": [
        " 数据基于加利福尼亚州 1990 年的人口普查数据。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TjLjL9IU80G",
        "colab_type": "text"
      },
      "source": [
        " ## 设置\n",
        "在此第一个单元格中，我们将加载必要的库。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVFf5asKE2Zt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import math\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipRyUHjhU80Q",
        "colab_type": "text"
      },
      "source": [
        " 接下来，我们将加载数据集。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ivCDWnwE2Zx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.cn/mledu-datasets/california_housing_train.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVk_qlG6U80j",
        "colab_type": "text"
      },
      "source": [
        " 我们将对数据进行随机化处理，以确保不会出现任何病态排序结果（可能会损害随机梯度下降法的效果）。此外，我们会将 `median_house_value` 调整为以千为单位，这样，模型就能够以常用范围内的学习速率较为轻松地学习这些数据。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0eVyguIU80m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "f50efd83-e21a-4e62-d477-977491030c92"
      },
      "source": [
        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
        "    np.random.permutation(california_housing_dataframe.index))\n",
        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
        "california_housing_dataframe"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11054</th>\n",
              "      <td>-121.0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>884.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>103.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13313</th>\n",
              "      <td>-121.9</td>\n",
              "      <td>37.3</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1335.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>635.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>345.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12749</th>\n",
              "      <td>-121.8</td>\n",
              "      <td>38.7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2875.0</td>\n",
              "      <td>810.0</td>\n",
              "      <td>1876.0</td>\n",
              "      <td>749.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>152.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14164</th>\n",
              "      <td>-122.1</td>\n",
              "      <td>37.6</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1644.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>1033.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>244.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14601</th>\n",
              "      <td>-122.2</td>\n",
              "      <td>37.4</td>\n",
              "      <td>34.0</td>\n",
              "      <td>2199.0</td>\n",
              "      <td>529.0</td>\n",
              "      <td>1193.0</td>\n",
              "      <td>532.0</td>\n",
              "      <td>4.3</td>\n",
              "      <td>405.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6026</th>\n",
              "      <td>-118.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>562.0</td>\n",
              "      <td>2105.0</td>\n",
              "      <td>503.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>152.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6031</th>\n",
              "      <td>-118.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1083.0</td>\n",
              "      <td>364.0</td>\n",
              "      <td>1132.0</td>\n",
              "      <td>338.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>153.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4015</th>\n",
              "      <td>-118.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1507.0</td>\n",
              "      <td>295.0</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>324.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>163.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5629</th>\n",
              "      <td>-118.2</td>\n",
              "      <td>34.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1629.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>1893.0</td>\n",
              "      <td>387.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>137.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3032</th>\n",
              "      <td>-117.8</td>\n",
              "      <td>33.8</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5436.0</td>\n",
              "      <td>707.0</td>\n",
              "      <td>2046.0</td>\n",
              "      <td>685.0</td>\n",
              "      <td>8.7</td>\n",
              "      <td>349.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  median_house_value\n",
              "11054     -121.0      37.6  ...            1.9               103.9\n",
              "13313     -121.9      37.3  ...            3.6               345.8\n",
              "12749     -121.8      38.7  ...            2.0               152.5\n",
              "14164     -122.1      37.6  ...            6.5               244.3\n",
              "14601     -122.2      37.4  ...            4.3               405.9\n",
              "...          ...       ...  ...            ...                 ...\n",
              "6026      -118.2      34.1  ...            2.0               152.1\n",
              "6031      -118.2      34.1  ...            2.2               153.1\n",
              "4015      -118.0      34.0  ...            4.1               163.3\n",
              "5629      -118.2      34.0  ...            2.3               137.5\n",
              "3032      -117.8      33.8  ...            8.7               349.5\n",
              "\n",
              "[17000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzzlSs3PtTmt",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "-"
        }
      },
      "source": [
        " ## 检查数据\n",
        "\n",
        "建议您在使用数据之前，先对它有一个初步的了解。\n",
        "\n",
        "我们会输出关于各列的一些实用统计信息快速摘要：样本数、均值、标准偏差、最大值、最小值和各种分位数。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzb10yoVrydW",
        "colab_type": "code",
        "slideshow": {
          "slide_type": "slide"
        },
        "cellView": "both",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          },
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f70b7272-6016-4352-ae68-60f9db14a2b7"
      },
      "source": [
        "california_housing_dataframe.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.6</td>\n",
              "      <td>35.6</td>\n",
              "      <td>28.6</td>\n",
              "      <td>2643.7</td>\n",
              "      <td>539.4</td>\n",
              "      <td>1429.6</td>\n",
              "      <td>501.2</td>\n",
              "      <td>3.9</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>12.6</td>\n",
              "      <td>2179.9</td>\n",
              "      <td>421.5</td>\n",
              "      <td>1147.9</td>\n",
              "      <td>384.5</td>\n",
              "      <td>1.9</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.8</td>\n",
              "      <td>33.9</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>297.0</td>\n",
              "      <td>790.0</td>\n",
              "      <td>282.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.5</td>\n",
              "      <td>34.2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2127.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.0</td>\n",
              "      <td>37.7</td>\n",
              "      <td>37.0</td>\n",
              "      <td>3151.2</td>\n",
              "      <td>648.2</td>\n",
              "      <td>1721.0</td>\n",
              "      <td>605.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.3</td>\n",
              "      <td>42.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>37937.0</td>\n",
              "      <td>6445.0</td>\n",
              "      <td>35682.0</td>\n",
              "      <td>6082.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  median_house_value\n",
              "count    17000.0   17000.0  ...        17000.0             17000.0\n",
              "mean      -119.6      35.6  ...            3.9               207.3\n",
              "std          2.0       2.1  ...            1.9               116.0\n",
              "min       -124.3      32.5  ...            0.5                15.0\n",
              "25%       -121.8      33.9  ...            2.6               119.4\n",
              "50%       -118.5      34.2  ...            3.5               180.4\n",
              "75%       -118.0      37.7  ...            4.8               265.0\n",
              "max       -114.3      42.0  ...           15.0               500.0\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr6wYl2bt2Ep",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "-"
        }
      },
      "source": [
        " ## 构建第一个模型\n",
        "\n",
        "在本练习中，我们将尝试预测 `median_house_value`，它将是我们的标签（有时也称为目标）。我们将使用 `total_rooms` 作为输入特征。\n",
        "\n",
        "**注意**：我们使用的是城市街区级别的数据，因此该特征表示相应街区的房间总数。\n",
        "\n",
        "为了训练模型，我们将使用 TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API 提供的 [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) 接口。此 API 负责处理大量低级别模型搭建工作，并会提供执行模型训练、评估和推理的便利方法。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0cpcsieFhsNI",
        "colab_type": "text"
      },
      "source": [
        " ### 第 1 步：定义特征并配置特征列"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EL8-9d4ZJNR7",
        "colab_type": "text"
      },
      "source": [
        " 为了将我们的训练数据导入 TensorFlow，我们需要指定每个特征包含的数据类型。在本练习及今后的练习中，我们主要会使用以下两类数据：\n",
        "\n",
        "* **分类数据**：一种文字数据。在本练习中，我们的住房数据集不包含任何分类特征，但您可能会看到的示例包括家居风格以及房地产广告词。\n",
        "\n",
        "* **数值数据**：一种数字（整数或浮点数）数据以及您希望视为数字的数据。有时您可能会希望将数值数据（例如邮政编码）视为分类数据（我们将在稍后的部分对此进行详细说明）。\n",
        "\n",
        "在 TensorFlow 中，我们使用一种称为“**特征列**”的结构来表示特征的数据类型。特征列仅存储对特征数据的描述；不包含特征数据本身。\n",
        "\n",
        "一开始，我们只使用一个数值输入特征 `total_rooms`。以下代码会从 `california_housing_dataframe` 中提取 `total_rooms` 数据，并使用 `numeric_column` 定义特征列，这样会将其数据指定为数值："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhEbFCZ86cDZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the input feature: total_rooms.\n",
        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
        "\n",
        "# Configure a numeric feature column for total_rooms.\n",
        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_3S8teX7Rd2",
        "colab_type": "text"
      },
      "source": [
        " **注意**：`total_rooms` 数据的形状是一维数组（每个街区的房间总数列表）。这是 `numeric_column` 的默认形状，因此我们不必将其作为参数传递。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMl3qrU5MGV6",
        "colab_type": "text"
      },
      "source": [
        " ### 第 2 步：定义目标"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw4nrfcB7kyk",
        "colab_type": "text"
      },
      "source": [
        " 接下来，我们将定义目标，也就是 `median_house_value`。同样，我们可以从 `california_housing_dataframe` 中提取它："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1NvvNkH8Kbt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the label.\n",
        "targets = california_housing_dataframe[\"median_house_value\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4M-rTFHL2UkA",
        "colab_type": "text"
      },
      "source": [
        " ### 第 3 步：配置 LinearRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUfGQUNp7jdL",
        "colab_type": "text"
      },
      "source": [
        " 接下来，我们将使用 LinearRegressor 配置线性回归模型，并使用 `GradientDescentOptimizer`（它会实现小批量随机梯度下降法 (SGD)）训练该模型。`learning_rate` 参数可控制梯度步长的大小。\n",
        "\n",
        "**注意**：为了安全起见，我们还会通过 `clip_gradients_by_norm` 将[梯度裁剪](https://developers.google.com/machine-learning/glossary/#gradient_clipping)应用到我们的优化器。梯度裁剪可确保梯度大小在训练期间不会变得过大，梯度过大会导致梯度下降法失败。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubhtW-NGU802",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a4879864-d5c0-4f8a-cae4-179e1a88613c"
      },
      "source": [
        "# Use gradient descent as the optimizer for training the model.\n",
        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "\n",
        "# Configure the linear regression model with our feature columns and optimizer.\n",
        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
        "linear_regressor = tf.estimator.LinearRegressor(\n",
        "    feature_columns=feature_columns,\n",
        "    optimizer=my_optimizer\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0IztwdK2f3F",
        "colab_type": "text"
      },
      "source": [
        " ### 第 4 步：定义输入函数"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5M5j6xSCHxx",
        "colab_type": "text"
      },
      "source": [
        " 要将加利福尼亚州住房数据导入 `LinearRegressor`，我们需要定义一个输入函数，让它告诉 TensorFlow 如何对数据进行预处理，以及在模型训练期间如何批处理、随机处理和重复数据。\n",
        "\n",
        "首先，我们将 *Pandas* 特征数据转换成 NumPy 数组字典。然后，我们可以使用 TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) 根据我们的数据构建 Dataset 对象，并将数据拆分成大小为 `batch_size` 的多批数据，以按照指定周期数 (num_epochs) 进行重复。\n",
        "\n",
        "**注意**：如果将默认值 `num_epochs=None` 传递到 `repeat()`，输入数据会无限期重复。\n",
        "\n",
        "然后，如果 `shuffle` 设置为 `True`，则我们会对数据进行随机处理，以便数据在训练期间以随机方式传递到模型。`buffer_size` 参数会指定 `shuffle` 将从中随机抽样的数据集的大小。\n",
        "\n",
        "最后，输入函数会为该数据集构建一个迭代器，并向 LinearRegressor 返回下一批数据。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKZ9zNcHJtwc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
        "    \"\"\"Trains a linear regression model of one feature.\n",
        "  \n",
        "    Args:\n",
        "      features: pandas DataFrame of features\n",
        "      targets: pandas DataFrame of targets\n",
        "      batch_size: Size of batches to be passed to the model\n",
        "      shuffle: True or False. Whether to shuffle the data.\n",
        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
        "    Returns:\n",
        "      Tuple of (features, labels) for next data batch\n",
        "    \"\"\"\n",
        "  \n",
        "    # Convert pandas data into a dict of np arrays.\n",
        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
        " \n",
        "    # Construct a dataset, and configure batching/repeating.\n",
        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    # Shuffle the data, if specified.\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(buffer_size=10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwa6UeA1V5F_",
        "colab_type": "text"
      },
      "source": [
        " **注意**：在后面的练习中，我们会继续使用此输入函数。有关输入函数和 `Dataset` API 的更详细的文档，请参阅 [TensorFlow 编程人员指南](https://www.tensorflow.org/programmers_guide/datasets)。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YS50CQb2ooO",
        "colab_type": "text"
      },
      "source": [
        " ### 第 5 步：训练模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yP92XkzhU803",
        "colab_type": "text"
      },
      "source": [
        " 现在，我们可以在 `linear_regressor` 上调用 `train()` 来训练模型。我们会将 `my_input_fn` 封装在 `lambda` 中，以便可以将 `my_feature` 和 `target` 作为参数传入（有关详情，请参阅此 [TensorFlow 输入函数教程](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model)），首先，我们会训练 100 步。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5M-Kt6w8U803",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_ = linear_regressor.train(\n",
        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
        "    steps=100\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Nwxqxlx2sOv",
        "colab_type": "text"
      },
      "source": [
        " ### 第 6 步：评估模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoDaF2dlJQG5",
        "colab_type": "text"
      },
      "source": [
        " 我们基于该训练数据做一次预测，看看我们的模型在训练期间与这些数据的拟合情况。\n",
        "\n",
        "**注意**：训练误差可以衡量您的模型与训练数据的拟合情况，但并**_不能_**衡量模型**_泛化到新数据_**的效果。在后面的练习中，您将探索如何拆分数据以评估模型的泛化能力。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDIxp6vcU809",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "8134e7ff-e950-4838-e63e-8a7085cd5c03"
      },
      "source": [
        "# Create an input function for predictions.\n",
        "# Note: Since we're making just one prediction for each example, we don't \n",
        "# need to repeat or shuffle the data here.\n",
        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
        "\n",
        "# Call predict() on the linear_regressor to make predictions.\n",
        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
        "\n",
        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "\n",
        "# Print Mean Squared Error and Root Mean Squared Error.\n",
        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error (on training data): 56367.025\n",
            "Root Mean Squared Error (on training data): 237.417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKWstXXPzOVz",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " 这是出色的模型吗？您如何判断误差有多大？\n",
        "\n",
        "由于均方误差 (MSE) 很难解读，因此我们经常查看的是均方根误差 (RMSE)。RMSE 的一个很好的特性是，它可以在与原目标相同的规模下解读。\n",
        "\n",
        "我们来比较一下 RMSE 与目标最大值和最小值的差值："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UwqGbbxP53O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "e164979f-0545-4a83-cada-93844df4f470"
      },
      "source": [
        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
        "min_max_difference = max_house_value - min_house_value\n",
        "\n",
        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
        "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
        "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min. Median House Value: 14.999\n",
            "Max. Median House Value: 500.001\n",
            "Difference between Min. and Max.: 485.002\n",
            "Root Mean Squared Error: 237.417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JigJr0C7Pzit",
        "colab_type": "text"
      },
      "source": [
        " 我们的误差跨越目标值的近一半范围，可以进一步缩小误差吗？\n",
        "\n",
        "这是每个模型开发者都会烦恼的问题。我们来制定一些基本策略，以降低模型误差。\n",
        "\n",
        "首先，我们可以了解一下根据总体摘要统计信息，预测和目标的符合情况。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "941nclxbzqGH",
        "colab_type": "code",
        "slideshow": {
          "slide_type": "-"
        },
        "cellView": "both",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          },
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "21fd0d81-fa71-498c-d637-974cd6f92a8c"
      },
      "source": [
        "calibration_data = pd.DataFrame()\n",
        "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "calibration_data[\"targets\"] = pd.Series(targets)\n",
        "calibration_data.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.1</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.1</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.2</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.9</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean           0.1    207.3\n",
              "std            0.1    116.0\n",
              "min            0.0     15.0\n",
              "25%            0.1    119.4\n",
              "50%            0.1    180.4\n",
              "75%            0.2    265.0\n",
              "max            1.9    500.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2-bf8Hq36y8",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "-"
        }
      },
      "source": [
        " 好的，此信息也许有帮助。平均值与模型的 RMSE 相比情况如何？各种分位数呢？\n",
        "\n",
        "我们还可以将数据和学到的线可视化。我们已经知道，单个特征的线性回归可绘制成一条将输入 *x* 映射到输出 *y* 的线。\n",
        "\n",
        "首先，我们将获得均匀分布的随机数据样本，以便绘制可辨的散点图。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGRIi3mAU81H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample = california_housing_dataframe.sample(n=300)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-JwuJBKU81J",
        "colab_type": "text"
      },
      "source": [
        " 然后，我们根据模型的偏差项和特征权重绘制学到的线，并绘制散点图。该线会以红色显示。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7G12E76-339G",
        "colab_type": "code",
        "slideshow": {
          "slide_type": "-"
        },
        "cellView": "both",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          },
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "3f713c68-c98d-4811-932c-754d23f1205c"
      },
      "source": [
        "# Get the min and max total_rooms values.\n",
        "x_0 = sample[\"total_rooms\"].min()\n",
        "x_1 = sample[\"total_rooms\"].max()\n",
        "\n",
        "# Retrieve the final weight and bias generated during training.\n",
        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "# Get the predicted median_house_values for the min and max total_rooms values.\n",
        "y_0 = weight * x_0 + bias \n",
        "y_1 = weight * x_1 + bias\n",
        "\n",
        "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
        "\n",
        "# Label the graph axes.\n",
        "plt.ylabel(\"median_house_value\")\n",
        "plt.xlabel(\"total_rooms\")\n",
        "\n",
        "# Plot a scatter plot from our data sample.\n",
        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
        "\n",
        "# Display graph.\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Uh2b1ouLsMmDssuuLyX27zG0ch5C390bRxMNmE3ORENoizvQs9bmykqbUpCyE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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0lRt4USU81L",
        "colab_type": "text"
      },
      "source": [
        " 这条初始线看起来与目标相差很大。看看您能否回想起摘要统计信息，并看到其中蕴含的相同信息。\n",
        "\n",
        "综上所述，这些初始健全性检查提示我们也许可以找到更好的线。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZWF67uv0HTG",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " ## 调整模型超参数\n",
        "对于本练习，为方便起见，我们已将上述所有代码放入一个函数中。您可以使用不同的参数调用该函数，以了解相应效果。\n",
        "\n",
        "我们会在 10 个等分的时间段内使用此函数，以便观察模型在每个时间段的改善情况。\n",
        "\n",
        "对于每个时间段，我们都会计算训练损失并绘制相应图表。这可以帮助您判断模型收敛的时间，或者模型是否需要更多迭代。\n",
        "\n",
        "此外，我们还会绘制模型随着时间的推移学习的特征权重和偏差项值的曲线图。您还可以通过这种方式查看模型的收敛效果。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgSMeD5UU81N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
        "  \"\"\"Trains a linear regression model of one feature.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: A `float`, the learning rate.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
        "      to use as input feature.\n",
        "  \"\"\"\n",
        "  \n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  my_feature = input_feature\n",
        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
        "  my_label = \"median_house_value\"\n",
        "  targets = california_housing_dataframe[my_label]\n",
        "\n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
        "  \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
        "  \n",
        "  # Create a linear regressor object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  linear_regressor = tf.estimator.LinearRegressor(\n",
        "      feature_columns=feature_columns,\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "\n",
        "  # Set up to plot the state of our model's line each period.\n",
        "  plt.figure(figsize=(15, 6))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.title(\"Learned Line by Period\")\n",
        "  plt.ylabel(my_label)\n",
        "  plt.xlabel(my_feature)\n",
        "  sample = california_housing_dataframe.sample(n=300)\n",
        "  plt.scatter(sample[my_feature], sample[my_label])\n",
        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training model...\")\n",
        "  print(\"RMSE (on training data):\")\n",
        "  root_mean_squared_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    linear_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Take a break and compute predictions.\n",
        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "    \n",
        "    # Compute loss.\n",
        "    root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(predictions, targets))\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
        "    # Finally, track the weights and biases over time.\n",
        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
        "    y_extents = np.array([0, sample[my_label].max()])\n",
        "    \n",
        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "    x_extents = (y_extents - bias) / weight\n",
        "    x_extents = np.maximum(np.minimum(x_extents,\n",
        "                                      sample[my_feature].max()),\n",
        "                           sample[my_feature].min())\n",
        "    y_extents = weight * x_extents + bias\n",
        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
        "  print(\"Model training finished.\")\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.ylabel('RMSE')\n",
        "  plt.xlabel('Periods')\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(root_mean_squared_errors)\n",
        "\n",
        "  # Output a table with calibration data.\n",
        "  calibration_data = pd.DataFrame()\n",
        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
        "  display.display(calibration_data.describe())\n",
        "\n",
        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kg8A4ArBU81Q",
        "colab_type": "text"
      },
      "source": [
        " ## 任务 1：使 RMSE 不超过 180\n",
        "\n",
        "调整模型超参数，以降低损失和更符合目标分布。\n",
        "约 5 分钟后，如果您无法让 RMSE 低于 180，请查看解决方案，了解可能的组合。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzoZUSdLIolF",
        "colab_type": "code",
        "slideshow": {
          "slide_type": "slide"
        },
        "cellView": "both",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          }
        }
      },
      "source": [
        "train_model(\n",
        "    learning_rate=0.00001,\n",
        "    steps=100,\n",
        "    batch_size=1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajVM7rkoYXeL",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看一种可能的解决方案。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3zmldDwYy5c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "outputId": "7a3b7e88-a847-445c-9d53-1f75db8ca751"
      },
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=500,\n",
        "    batch_size=5\n",
        ")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 225.63\n",
            "  period 01 : 214.42\n",
            "  period 02 : 204.04\n",
            "  period 03 : 194.97\n",
            "  period 04 : 186.92\n",
            "  period 05 : 180.53\n",
            "  period 06 : 175.66\n",
            "  period 07 : 172.08\n",
            "  period 08 : 169.46\n",
            "  period 09 : 167.53\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>115.3</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>95.0</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.1</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63.7</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>92.7</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>137.4</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1654.1</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         115.3    207.3\n",
              "std           95.0    116.0\n",
              "min            0.1     15.0\n",
              "25%           63.7    119.4\n",
              "50%           92.7    180.4\n",
              "75%          137.4    265.0\n",
              "max         1654.1    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Final RMSE (on training data): 167.53\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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RERERiTtKWIiIiIiIiIhI3FHCQkRERERERETijhIWIiIiIiIiIhJ3lLAQERERERERkbiT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\ntCP4YYYFNO1V/oY0dSMdyIqIcN8U+3OcrL+VGW0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s2UW0QceYvmnBDktRFKXd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ROc4E0qq28dGXfkNbUgoH7r+T7xXV0YWNuDs/SXfAR6C5ngRXJlZ3PrtrFCcbNY54WDpdYY2z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\nEEIIIYQQIuZIwEIIIYQQQgghhBAx5/8HZQpbX2CwsP8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8H0_D4vYa49",
        "colab_type": "text"
      },
      "source": [
        " 这只是一种可能的配置；也许还有同样能够提供理想结果的其他设置组合。请注意，总体而言，本练习重点不是查找*一种最佳*设置，而是帮助您对模型配置调整如何影响预测质量有一个直观的认识。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU5sLyYTqzqL",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " ### 有适用于模型调整的标准启发法吗？\n",
        "\n",
        "这是一个常见的问题。简短的答案是，不同超参数的效果取决于数据。因此，不存在必须遵循的规则，您需要对自己的数据进行测试。\n",
        "\n",
        "即便如此，我们仍在下面列出了几条可为您提供指导的经验法则：\n",
        "\n",
        " * 训练误差应该稳步减小，刚开始是急剧减小，最终应随着训练收敛达到平稳状态。\n",
        " * 如果训练尚未收敛，尝试运行更长的时间。\n",
        " * 如果训练误差减小速度过慢，则提高学习速率也许有助于加快其减小速度。\n",
        "   * 但有时如果学习速率过高，训练误差的减小速度反而会变慢。\n",
        " * 如果训练误差变化很大，尝试降低学习速率。\n",
        "   * 较低的学习速率和较大的步数/较大的批量大小通常是不错的组合。\n",
        " * 批量大小过小也会导致不稳定情况。不妨先尝试 100 或 1000 等较大的值，然后逐渐减小值的大小，直到出现性能降低的情况。\n",
        "\n",
        "重申一下，切勿严格遵循这些经验法则，因为效果取决于数据。请始终进行试验和验证。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GpV-uF_cBCBU",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " ## 任务 2：尝试其他特征\n",
        "\n",
        "使用 `population` 特征替换 `total_rooms` 特征，看看能否取得更好的效果。\n",
        "\n",
        "这部分不必超过 5 分钟。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMyOxzb0ZlAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# YOUR CODE HERE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ci1ISxxrZ7v0",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看一种可能的解决方案。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjdQQCduZ7BV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "outputId": "98c89a06-b963-41f5-c90e-b7d363e711a8"
      },
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=1000,\n",
        "    batch_size=5,\n",
        "    input_feature=\"population\"\n",
        ")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 225.63\n",
            "  period 01 : 214.84\n",
            "  period 02 : 205.05\n",
            "  period 03 : 196.42\n",
            "  period 04 : 189.80\n",
            "  period 05 : 184.68\n",
            "  period 06 : 181.27\n",
            "  period 07 : 178.55\n",
            "  period 08 : 176.92\n",
            "  period 09 : 176.04\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>119.2</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>95.7</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.3</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>65.9</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>97.3</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>143.5</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2975.9</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         119.2    207.3\n",
              "std           95.7    116.0\n",
              "min            0.3     15.0\n",
              "25%           65.9    119.4\n",
              "50%           97.3    180.4\n",
              "75%          143.5    265.0\n",
              "max         2975.9    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Final RMSE (on training data): 176.04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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QQgjbkYSFEEIIIYQQQgghbEcSFkIIIYQQQgghhLAdSVgIIYQQQgghhBDCdiRhIYQQQggh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aauAsHvo1oayspwtLllfUGUy2GyR2PlfbWNw/OeI3TsVfRb8vcGN8KW/CzKP38L4emD19RHcLGz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\nieH7dZgO7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gnE13NfM/CUlHKfEMIX2CuE2ALMBL6VUi4UQiwAFgB/Bm7CNja1OzAceLPq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\nIYQQQgjR6/x/UFVT6Gci+oEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}